Pure Functions: A function that returns the same output given the same input and has no side effects.
Immutability: The practice of not changing the state of objects once they are created.
Higher-Order Functions: Functions that take one or more functions as arguments, or return a function as its result.
Recursion: A technique of defining a function in terms of itself.
Function Composition: A technique of combining multiple functions into a single function.
Lazy Evaluation: Delaying the evaluation of an expression until it is actually needed.
Referential Transparency: A property of pure functions, where a function call can be replaced with its return value without affecting the program’s behavior.
Pattern Matching: A technique of matching data structures against predefined patterns.
Currying: Transforming a function that takes multiple arguments into a series of functions that each take a single argument.
Advantages
Improved code quality: In functional programming, functions are designed to have no side effects and to be idempotent, meaning that given the same input, the function will always produce the same output. This makes the code more predictable and easier to test, debug, and maintain.
Increased modularity: Functional programming encourages the use of small, reusable functions that can be composed to build larger functions. This makes the code more modular and easier to reason about.
Parallelism: Functional programming promotes immutability, which makes it easier to write parallel and concurrent code. Because functional programs don’t have shared state, there are no race conditions, deadlocks, or other concurrency issues to worry about.
Abstraction: Functional programming provides powerful abstraction mechanisms, such as higher-order functions, that allow developers to write generic code that can be reused across many different contexts.
Easier debugging: Because functional programming emphasizes pure functions and immutability, it is easier to reason about the behavior of a program and to debug problems when they arise.
Better performance: Functional programming can lead to better performance because pure functions are easier to optimize and can be parallelized more easily.
Disadvantages
Steep learning curve: Functional programming can be challenging to learn, especially for developers who are used to imperative or object-oriented programming. The concepts of pure functions, immutability, and higher-order functions may take some time to grasp.
Limited ecosystem: Functional programming languages and libraries may not have the same level of support and community as more mainstream programming languages like Java or Python. This can make it harder to find resources, tools, and frameworks to help you build your application.
Performance overhead: Functional programming languages often rely on immutable data structures and other functional constructs that can have a performance overhead compared to imperative or object-oriented languages. This can be especially true when dealing with large data sets.
Debugging difficulties: While functional programming can make code easier to reason about, it can also make debugging more challenging. Because functions are pure and don’t have side effects, it can be harder to figure out where a problem is coming from.
Difficulty with certain types of problems: Functional programming is not always the best choice for solving certain types of problems, such as those that involve a lot of mutable state or require low-level control over hardware.
Lack of support for imperative programming: Functional programming languages often lack support for imperative programming constructs, such as loops and mutable variables. This can make it harder to write code for certain types of applications, such as games or real-time systems.